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https://github.com/spidy21/medical_cost_prediction
https://github.com/spidy21/medical_cost_prediction
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- Host: GitHub
- URL: https://github.com/spidy21/medical_cost_prediction
- Owner: SpidY21
- Created: 2024-04-25T19:59:22.000Z (7 months ago)
- Default Branch: master
- Last Pushed: 2024-10-28T18:57:11.000Z (22 days ago)
- Last Synced: 2024-10-28T19:44:48.446Z (22 days ago)
- Language: Python
- Size: 7.58 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Health Insurance Charges Dataset
## Overview
This dataset contains information about individuals' health, lifestyle, and demographic factors that could influence their health insurance charges. The dataset can be used to analyze and predict the cost of health insurance for an individual based on several key factors, such as age, sex, BMI, number of children, smoking status, and region.
## Dataset Description
The dataset consists of 7 columns, each representing different attributes of the individuals. The primary variable of interest is `charges`, which indicates the health insurance cost for each individual. Below is a description of each column:
| Column | Description |
|------------|-------------------------------------------------------------------------------------------------------|
| `age` | Age of the individual in years. |
| `sex` | Gender of the individual (male or female). |
| `bmi` | Body Mass Index (BMI), which is a measure of body fat based on height and weight. |
| `children` | Number of children or dependents covered by the insurance plan. |
| `smoker` | Smoking status of the individual (yes or no). |
| `region` | The region in the U.S. where the individual resides (northeast, northwest, southeast, southwest). |
| `charges` | The medical insurance charges billed to the individual. |
## Objective
The objective of this dataset is to build predictive models to estimate the insurance charges for individuals based on the available features. This can help insurance companies or policymakers understand how different factors, like age, smoking status, or BMI, impact insurance costs. Additionally, individuals can use this information to understand the factors affecting their health insurance costs and potentially take steps to reduce them.
## Potential Applications
- **Predictive Modeling**: Build machine learning models to predict insurance charges for individuals based on their attributes.
- **Exploratory Data Analysis (EDA)**: Identify patterns or correlations between variables, such as how smoking or BMI affects insurance costs.
- **Healthcare Policy Insights**: Gain insights into which demographic or lifestyle factors most significantly influence health insurance costs, helping policymakers target health interventions.
## Key Columns to Explore
1. **Age**: Insurance charges may increase with age as older individuals generally have higher healthcare costs.
2. **BMI**: Higher BMI could be correlated with higher insurance charges due to increased health risks.
3. **Smoking Status**: Smokers may have higher insurance charges, as smoking is associated with numerous health risks.
4. **Region**: Regional differences could be explored to understand if healthcare costs vary across different areas.
## Usage
This dataset can be used for both supervised learning and exploratory analysis to understand the factors that drive medical insurance costs. It can also be valuable for practicing regression analysis, as the `charges` column is a continuous numerical variable that can serve as the target variable for predictive modeling.
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By analyzing this dataset, you can gain insights into how various factors impact health insurance charges, and develop predictive models that estimate costs based on individual characteristics.
This is the deployed link for the app https://spidy21-medical-cost-prediction-app-aht1f2.streamlit.app/